Replication Data for: High-dimensional neural network potentials for accurate vibrational frequencies: The formic acid dimer benchmark

Replication data for the publication D. S. Rasheeda, A. M. Stanta Daría, B. Schröder, E. Mátyus, J. Behler, High-dimensional neural network potentials for accurate vibrational frequencies: The formic acid dimer benchmark, Phys. Chem. Chem. Phys., 2022. The data set contains the following: - new calculated ab initio points (fc-CCSD(T)-F12a/haTZ) used for training of FAD-HDNNP Filename: new_abinitio_points.dat.gz - FAD-HDNNP input files for the RuNNer program Filename: FAD-HDNNP_RuNNer_input.tar.gz - parameters of the QFF (equilibrium geometry, normal coordinates and force constants) based on FAD-HDNNP Filename: FAD-HDNNP_QFF.tar.gz - data points for numerical differentiation that determine the reference ab initio QFF (fc-CCSD(T)-F12a/haTZ) Filename: Ref_abinitio_QFF_points.dat.gz - parameters of the reference ab initio QFF (equilibrium geometry, normal coordinates and force constants) Filename: Ref_abinitio_QFF.tar.gz